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UX Mantra I received from Mantra Labs

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4 minutes read

My learnings and experiences as a UI/UX intern at Mantra Labs.

UX Mantra I received from Mantra Labs

“Design creates culture. Culture shapes values. Values determine the future.” — Robert L. Peters, designer and author

In this blog, I will share my learnings and experiences working alongside the design team at Mantra Labs.

The past three months working at Mantra Labs as UI/UX intern have been one of the most memorable times of my life, from meeting some amazing folks to learning some super cool tips and tricks. It was undoubtedly an amazing experience. Most people believe that interns only work on dummy tasks and never make any impact on the company despite their hard work. At Mantra Labs, this aspect is absolutely untrue. In a very short interval of time, I worked on multiple projects from different domains. As an intern, I was given the opportunity to lead a project’s UI/UX design from start to launch. I had to take all the design decisions, interact with stakeholders, collaborate with developers, and manage even the simplest of tasks involved.

Here are some of the key learnings from my experience at the company:

1. Ask Questions

The best way to accomplish something is to ask lots of questions to be sure what exactly needs to be accomplished. To be honest, in the beginning, I didn’t know the exact way to do lots of things. But, as an overthinker, I was always concerned about not being annoying. My manager and colleagues showed humility and taught me every little thing with utter patience. There were times when my manager got into some other work which left no time for him to answer my questions. Even then, I had my lovely teammates who stood there to guide me. I learned the most from my internship by asking questions and clarifying all my doubts.

2. Keep an open mind and apply a positive approach.

UI/UX Designing is incomplete without solving problems. The client’s requirements must be met by all means while keeping accessibility, conversational and humanized approach, and all the other things in mind. Therefore, for such a task, the ability to hear all the reviews and perspectives with an open mind and apply a positive approach to it is the only key.

Working with different design minds at Mantra Labs made me understand that not everyone is going to agree with your designs and ideas — even people in your own team! One has to keep iterating, once, twice, and sometimes even ten times. No matter how many changes happen, they should not be taken personally because a majority of the time the changes are only going to improve the product in the end.

3. Stop over-evaluating!

I have always been someone who at every step has over-evaluated myself. Thriving to achieve the best of me has been overwhelming all my life. Here, at Mantra Labs, I learned how to trust my instincts as far as designing was concerned. I was corrected wherever I made wrong decisions. It all made sense when I saw the outcome. It was during my internship that I learned how important it is to always check all decisions, but never question yourself to the point where you lose interest in your own judgment.

4. Try something new, and explore different domains.

Ever since I started my career, I was mainly working with Ed-tech companies but at Mantra Labs, I got the opportunity to work in multiple domains like Health Tech, and Solar Tech in a very short duration of time. Obviously, these fields were quite different as these domains were very new to me but as a UI/UX Designer, you’ll have to be ready to solve any problems irrespective of any domain.

5. Show gratitude

An entire team is involved to complete a project. You win only when everyone in the team applies equal effort to make it happen (it’s the teamwork that counts). The work culture in Mantra Labs is great, from cool colleagues to a cooler manager. All of them work and coordinate with each other in a way that ultimately leads to the completion of the project to the satisfaction of the client. Having such people around me at work was no less than a blessing during my internship.

Better Communication skills

Communication requires a language common to the speaker and the listener. Fortunately or unfortunately that language is English. Honestly, this language has not been a very good friend of mine. I was quite good with one-on-one conversations but public speaking had mostly been a blunder. During the course of my internship, I led some client meetings and also demonstrated my work to a group of people. Talking to clients and my teammates have helped me brush my communication skills and instilled in me a sense of confidence.

Any sort of work can become boring if one stops taking fun-filled breaks from it. UI/UX Designing is a creative field and creativity comes only with the freshness of mind. I am someone who is a workaholic, I skip my meals and sleep until the work assigned to me is completed. There were instances during my internship when I would get so involved in the project that I used to forget to get myself engaged with my fellow teammates and colleagues. At Mantra Labs, the environment was so cool and friendly that we played numerous games (treasure hunt being at the top of my list) between work. We even celebrated each other’s birthdays and partied after the office. All these were a sort of my recreation to get back to work the next day with the same zeal and zest.

Before wrapping this up, let me tell you something very important:-

Design overthinking is now extremely common among designers. A deep design thinking approach is not always necessary when solving problems, the solution to some issues can be as simple as drawing rectangles.

Draw rectangles, Don’t overthink 🚀

About the author:  

Shashi Kumar is a pre-final year journalism student at Chandigarh University, who worked with Mantra Labs as a UI/UX design intern. He loves to talk about geopolitics and entrepreneurship.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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